Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification method
One of the core challenges of decision research is to identify individuals’ decision strategies without influencing decision behavior by the method used. Bröder and Schiffer (2003) suggested a method to classify decision strategies based on a maximum likelihood estimation, comparing the probability...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2009-04-01
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| Series: | Judgment and Decision Making |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S1930297500001728/type/journal_article |
| _version_ | 1797701742414004224 |
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| author | Andreas Glöckner |
| author_facet | Andreas Glöckner |
| author_sort | Andreas Glöckner |
| collection | DOAJ |
| description | One of the core challenges of decision research is to identify individuals’ decision strategies without influencing decision behavior by the method used. Bröder and Schiffer (2003) suggested a method to classify decision strategies based on a maximum likelihood estimation, comparing the probability of individuals’ choices given the application of a certain strategy and a constant error rate. Although this method was shown to be unbiased and practically useful, it obviously does not allow differentiating between models that make the same predictions concerning choices but different predictions for the underlying process, which is often the case when comparing complex to simple models or when comparing intuitive and deliberate strategies. An extended method is suggested that additionally includes decision times and confidence judgments in a simultaneous Multiple-Measure Maximum Likelihood estimation. In simulations, it is shown that the method is unbiased and sensitive to differentiate between strategies if the effects on times and confidence are sufficiently large. |
| first_indexed | 2024-03-12T04:40:07Z |
| format | Article |
| id | doaj.art-824c5e001e1e44b89cb9813d5e2d09ba |
| institution | Directory Open Access Journal |
| issn | 1930-2975 |
| language | English |
| last_indexed | 2024-03-12T04:40:07Z |
| publishDate | 2009-04-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Judgment and Decision Making |
| spelling | doaj.art-824c5e001e1e44b89cb9813d5e2d09ba2023-09-03T09:45:42ZengCambridge University PressJudgment and Decision Making1930-29752009-04-01418619910.1017/S1930297500001728Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification methodAndreas Glöckner0Max Planck Institute for Research on Collective GoodsOne of the core challenges of decision research is to identify individuals’ decision strategies without influencing decision behavior by the method used. Bröder and Schiffer (2003) suggested a method to classify decision strategies based on a maximum likelihood estimation, comparing the probability of individuals’ choices given the application of a certain strategy and a constant error rate. Although this method was shown to be unbiased and practically useful, it obviously does not allow differentiating between models that make the same predictions concerning choices but different predictions for the underlying process, which is often the case when comparing complex to simple models or when comparing intuitive and deliberate strategies. An extended method is suggested that additionally includes decision times and confidence judgments in a simultaneous Multiple-Measure Maximum Likelihood estimation. In simulations, it is shown that the method is unbiased and sensitive to differentiate between strategies if the effects on times and confidence are sufficiently large.https://www.cambridge.org/core/product/identifier/S1930297500001728/type/journal_articlestrategy classificationjudgmentdecision makingmaximum likelihood estimationintuition |
| spellingShingle | Andreas Glöckner Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification method Judgment and Decision Making strategy classification judgment decision making maximum likelihood estimation intuition |
| title | Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification method |
| title_full | Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification method |
| title_fullStr | Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification method |
| title_full_unstemmed | Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification method |
| title_short | Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification method |
| title_sort | investigating intuitive and deliberate processes statistically the multiple measure maximum likelihood strategy classification method |
| topic | strategy classification judgment decision making maximum likelihood estimation intuition |
| url | https://www.cambridge.org/core/product/identifier/S1930297500001728/type/journal_article |
| work_keys_str_mv | AT andreasglockner investigatingintuitiveanddeliberateprocessesstatisticallythemultiplemeasuremaximumlikelihoodstrategyclassificationmethod |